binsreg 3.1.0

Implements binscatter methods, including partition selection, point estimation, pointwise and uniform inference methods, and graphical procedures.

BINSREG

The binsreg package provides tools for statistical analysis using the binscatter methods.

• binsreg: implements binscatter least squares regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band.
• binsqreg: implements binscatter quantile regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band.
• binsglm: implements binscatter generalized linear regression with robust inference and plots, including curve estimation, pointwise confidence intervals and uniform confidence band.
• binstest: implements binscatter-based hypothesis testing procedures for parametric specifications of and shape restrictions on the unknown function of interest.
• binspwc: implements hypothesis testing procedures for pairwise group comparison of binscatter estimators.
• binsregselect: implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning.

All the commands allow for covariate adjustment, smoothness restrictions, and clustering, among other features. See Cattaneo, Crump, Farrell and Feng (2024, 2025, 2026) for references.

Website: https://nppackages.github.io/.

Source code: https://github.com/nppackages/binsreg.

Authors

Matias D. Cattaneo ([e-mail])

Richard K. Crump ([e-mail])

Max H. Farrell ([e-mail])

Yingjie Feng ([e-mail])

Ricardo Masini ([e-mail])

Installation

To install/update use pip

pip install binsreg

Usage

from binsreg import binsregselect, binsreg, binsqreg, binsglm, binstest, binspwc

• Replication: binsreg illustration, plot illustration, simulated data.

Dependencies

• numpy
• pandas
• scipy
• statsmodels
• plotnine

References

For overviews and introductions, see NP Packages website.

Software and Implementation

• Cattaneo, Crump, Farrell and Feng (2025): Binscatter Regressions.
  Stata Journal 25(1): 3-50.

Technical and Methodological

• Cattaneo, Crump, Farrell and Feng (2024): On Binscatter.
  American Economic Review 114(5): 1488-1514.
  Supplemental Appendix

• Cattaneo, Crump, Farrell and Feng (2026): Nonlinear Binscatter Methods.
  Review of Economics and Statistics, revise and resubmit.
  Supplemental Appendix